{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Condition 1</th>\n",
       "      <th>Condition 2</th>\n",
       "      <th>Condition 3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.696469</td>\n",
       "      <td>0.570961</td>\n",
       "      <td>0.686348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.286139</td>\n",
       "      <td>0.764489</td>\n",
       "      <td>0.127180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.226851</td>\n",
       "      <td>0.652010</td>\n",
       "      <td>0.349014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.551315</td>\n",
       "      <td>0.549921</td>\n",
       "      <td>0.456309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.719469</td>\n",
       "      <td>0.650199</td>\n",
       "      <td>0.952940</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Condition 1  Condition 2  Condition 3\n",
       "0     0.696469     0.570961     0.686348\n",
       "1     0.286139     0.764489     0.127180\n",
       "2     0.226851     0.652010     0.349014\n",
       "3     0.551315     0.549921     0.456309\n",
       "4     0.719469     0.650199     0.952940"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(123)\n",
    "df = pd.DataFrame({'Condition 1':np.random.rand(20),\n",
    "                   'Condition 2':np.random.rand(20)*0.9,\n",
    "                   'Condition 3':np.random.rand(20)*1.1})\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "df.plot.bar(ax=ax)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "df.plot.bar(ax=ax, stacked=True) #堆叠，看总体情况\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.10"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
